An Analysis of Factors Affecting Korean Banks' Maturity Mismatch (in Korean)
Bibliographic record
Abstract
This paper measures banks' maturity mismatches using 「(short term funding - short term fund management)/total funding〠ratio. The domestic banks' average maturity mismatch ratio had increased significantly since mid-2000s until the third quarter of 2008, in which the global financial crisis broke out. Meanwhile, this paper finds that domestic banks whose maturity mismatch rates were higher before the financial crisis received greater amount of liquidity support from the government and the central bank after the financial crisis. The result of theoretical analysis using the general equilibrium model shows that a variety of factors including agents' risk aversions affects a bank's maturity mismatch. According to the empirical analysis, the lower the core capital ratio, an indicator reflecting banks' risk management efforts, the greater is the maturity mismatch. And, increases in prices of residential housing and stocks, which are substitute for long term deposits, bring rise in the maturity mismatch. Finally, the higher the severity of competition among banks and the lower the maturity spread, the greater is the maturity mismatch.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".